Scalable centrifugal force made it possible to adjust the injection speed of the organic solvent into the aqueous solution in the DLLME step by changing the spin speed. Spin speed of 100&nbsp;rpm was used in dispersion step and spin speed of 3500&nbsp;rpm was used to sediment organic phase in DLLME step. The proposed device provides effective and reproducible extraction using a low volume of the sample solution. After optimization of the effective parameters, an EME-DLLME followed by GC-MS was performed for determination of amitriptyline and imipramine in saliva, urine, and blood plasma samples. The method provides extraction recoveries and preconcentration factors in the range of 43%-70.8% and 21.5-35.5 respectively. The detection limits less than 0.5&nbsp;μg&nbsp;L-1 with the relative standard deviations of the analysis which were found in the range of 1.9%-3.5% (n&nbsp;=&nbsp;5). The method is suitable for drug monitoring and analyzing biofluids containing low levels of the model analytes. Multi-target detection has been widely applied for the sensitive measurement of cancer-related biomarkers; however, the design and application of single platforms for diverse target detection are still challenging. Herein, a robust and sensitive electrochemiluminescence (ECL) biosensing platform was constructed for the measurement of microRNA-21 (miRNA-21) and mucin 1 (MUC1) based on dual catalytic hairpin assembly (DCHA). The catalytic hairpin assembly (CHA) process (Cycle I) was initiated by the target miRNA-21 to introduce abundant CdSMn quantum dots (CdSMn QDs) on the electrode surface, leading to a considerable ECL response and the sensitive detection of miRNA-21 with a limit of detection as low as 11 aM. Subsequently, the second CHA process (Cycle II) was triggered by the MUC1-aptamer complex, which allowed copious amounts of Au nanoparticles (AuNPs) to approach the CdSMn QDs. A decreased ECL signal was obtained due to the ECL resonance energy transfer (ECL-RET) effect between the CdSMn QDs and AuNPs; meanwhile, MUC1 was sensitively detected with a limit of detection of 0.40&nbsp;fg&nbsp;mL-1. This single sensing platform achieved dual cancer-related biomarker detection, which could provide a rational approach for future clinical analyse. Electrochemical sensing is an effective, low-cost technology for cancer detection. In this study, mesoporous TiO2 was prepared via biomimetic synthesis based on yeast cell templates, and used to prepare a modified electrode for the sensitive detection of pancreatic cancer miR-1290. The structure and the morphology of the TiO2 were characterized by X-ray diffraction (XRD), N2 adsorption-desorption isotherm (NADI), Atomic force microscopy (AFM), and electron probe microanalysis (EPMA). As a sensing active material, the yeast-templated mesoporous TiO2 could detect pancreatic cancer miRNAs with single-nucleotide discrimination. The sample prepared by calcination at 400&nbsp;°C showed the best electrochemical sensing activity. Moreover, compared with the blank electrode, the yeast mesoporous TiO2 sensing electrode could oxidize the pancreatic cancer microRNAs at a lower potential, which minimized the interference from oxygen evolution reaction at high potentials. Simultaneous recording of action potentials (APs) and neurotransmitter release is highly desirable in living neurons since it provides a complete framework of the physiological and pathological statuses of nerve cells. In this work, we proposed an approach coupling ultra-thin microelectrode array (MEA) with total internal reflection fluorescence microscopy (TIRFM), which served as a powerful platform to visualize both APs and vesicular exocytosis in a neuronal circuit model formed by neuron-like PC12&nbsp;cells. Taking advantages of fluorescent false neurotransmitter (FFN), the transient neurotransmitter transport down an axon could be visualized with high spatial and temporal resolution. https://www.selleckchem.com/products/bms-986165.html The real-time recording of APs burst and neurotransmitter release induced by hypoxia with MEA/TIRFM platform reveals the relevance of electrical and chemical activities in the neuronal model. The combination of the optical and electrical techniques enables mapping of neuron connectivity in an entire neuronal circuit, which may ultimately lead to deeper understanding of nervous system. Multivariate curve resolution (MCR) is a powerful tool in chemometrics that has been involved in the solution of many analytical problems. The introduction of partial or incomplete knowledge of reference values as known-value constraints in an MCR model can considerably reduce the extent of rotational ambiguity for all components. Known-value constraints can provide enough information for MCR methods to perform both the identification and quantitative analysis of first-order data sets. In practice, in addition to noise and non-ideal behavior, limitations in the reference methods or procedures cause deviation in measured known values. It is shown that deviation in the measured known values, when used as known-value constraints, may result in considerable quantification errors in MCR results and can challenge identification analysis. This contribution investigates the importance and effect of soft known-value constraints on the accuracy of MCR solutions. The influence of noise levels, the amount of deviation of known values from true values, and the interaction of these two factors were evaluated with simulated data. An illustration using soft known-value constraints is given for a batch reaction experiment. Reference materials are used in diffuse reflectance imaging for transforming the digitized camera signal into reflectance and absorbance units for subsequent interpretation. Traditional white and dark reference signals are generally used for calculating reflectance or absorbance, but these can be supplemented with additional reflectance targets to improve the accuracy of reflectance transformations. In this work we provide an overview of hyperspectral image regression and assess the effects of reflectance calibration on image interpretation using partial least squares regression. Linear and quadratic reflectance transformations based on additional reflectance targets decrease average measurement errors and make it easier to estimate model pseudorank during image regression. The lowest measurement and prediction errors were obtained with the column and wavelength specific quadratic transformations which retained the spatial information provided by the line-scanning instrument and reduced errors in the predicted concentration maps.